Co-location Detection on the Cloud

This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians’ final decisions.

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Robust location detection in emergency sensor networks

IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428) ◽

10.1109/infcom.2003.1208941 ◽

2003 ◽

Cited By ~ 69

Author(s):

S. Ray ◽

R. Ungrangsi ◽

De Pellegrini ◽

A. Trachtenberg ◽

D. Starobinski

Keyword(s):

Sensor Networks ◽

Location Detection

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LORE: a model for the detection of fine-grained locative references in tweets

10.7764/onomazein.52.11 ◽

2021 ◽

Author(s):

Nicolás José Fernández-Martínez ◽

◽

Carlos Periñán-Pascual ◽

Keyword(s):

Emergency Services ◽

Situational Awareness ◽

State Of The Art ◽

Detection System ◽

Linguistic Knowledge ◽

Terrorist Attacks ◽

Fine Grained ◽

Points Of Interest ◽

Car Accidents ◽

Location Detection

Extracting geospatially rich knowledge from tweets is of utmost importance for location-based systems in emergency services to raise situational awareness about a given crisis-related incident, such as earthquakes, floods, car accidents, terrorist attacks, shooting attacks, etc. The problem is that the majority of tweets are not geotagged, so we need to resort to the messages in the search of geospatial evidence. In this context, we present LORE, a location-detection system for tweets that leverages the geographic database GeoNames together with linguistic knowledge through NLP techniques. One of the main contributions of this model is to capture fine-grained complex locative references, ranging from geopolitical entities and natural geographic references to points of interest and traffic ways. LORE outperforms state-of-the-art open-source location-extraction systems (i.e. Stanford NER, spaCy, NLTK and OpenNLP), achieving an unprecedented trade-off between precision and recall. Therefore, our model provides not only a quantitative advantage over other well-known systems in terms of performance but also a qualitative advantage in terms of the diversity and semantic granularity of the locative references extracted from the tweets.

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Priming from the Attentional Blink: A Failure to Extract Visual Tokens but Not Visual Types

Psychological Science ◽

10.1111/j.1467-9280.1997.tb00689.x ◽

1997 ◽

Vol 8 (2) ◽

pp. 95-100 ◽

Cited By ~ 134

Author(s):

Kimron Shapiro ◽

Jon Driver ◽

Robert Ward ◽

Robyn E. Sorensen

Keyword(s):

Attentional Blink ◽

Semantic Priming ◽

Visual Processing ◽

Visual Representation ◽

Stimulus Processing ◽

Visual Events ◽

Early Visual Processing ◽

Location Detection ◽

Subsequent Target ◽

Initial Target

When people must detect several targets in a very rapid stream of successive visual events at the same location, detection of an initial target induces misses for subsequent targets within a brief period. This attentional blink may serve to prevent interruption of ongoing target processing by temporarily suppressing vision for subsequent stimuli. We examined the level at which the internal blink operates, specifically, whether it prevents early visual processing or prevents quite substantial processing from reaching awareness. Our data support the latter view. We observed priming from missed letter targets, benefiting detection of a subsequent target with the same identity but a different case. In a second study, we observed semantic priming from word targets that were missed during the blink. These results demonstrate that attentional gating within the blink operates only after substantial stimulus processing has already taken place. The results are discussed in terms of two forms of visual representation, namely, types and tokens.

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S-Transform Aided Random Forest Based PD Location Detection Employing Signature of Optical Sensor

IEEE Transactions on Power Delivery ◽

10.1109/tpwrd.2018.2880244 ◽

2019 ◽

Vol 34 (4) ◽

pp. 1261-1268 ◽

Cited By ~ 3

Author(s):

Subimal Bag ◽

Arpan Kumar Pradhan ◽

Santanu Das ◽

Sovan Dalai ◽

Biswendu Chatterjee

Keyword(s):

Random Forest ◽

Optical Sensor ◽

S Transform ◽

Location Detection

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